Posterior predictive checks — check_predictions

Posterior predictive checks mean "simulating replicated data under the fitted model and then comparing these to the observed data" (Gelman and Hill, 2007, p. 158). Posterior predictive checks can be used to "look for systematic discrepancies between real and simulated data" (Gelman et al. 2014, p. 169). performance provides posterior predictive check methods for a variety of frequentist models (e.g., lm, merMod, glmmTMB, ...). For Bayesian models, the model is passed to bayesplot::pp_check(). If check_predictions() doesn't work as expected, try setting verbose = TRUE to get hints about possible problems.

Case Study: Understanding your models

Automated Reporting of Results and Statistical Models

The aim of the report package is to bridge the gap between R’s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.

modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions

Makowski et al., (2025). modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions. Journal of Open Source Software, 10(109), 7969, https://doi.org/10.21105/joss.07969

Evaluating Evidence and Making Decisions using Bayesian Statistics